from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-22 14:04:26.806129
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 22, Dec, 2021
Time: 14:04:31
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.5792
Nobs: 513.000 HQIC: -48.0315
Log likelihood: 5933.63 FPE: 1.03173e-21
AIC: -48.3231 Det(Omega_mle): 8.67178e-22
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.354171 0.078126 4.533 0.000
L1.Burgenland 0.099620 0.043678 2.281 0.023
L1.Kärnten -0.115519 0.022542 -5.125 0.000
L1.Niederösterreich 0.178863 0.090725 1.971 0.049
L1.Oberösterreich 0.123833 0.091470 1.354 0.176
L1.Salzburg 0.281343 0.047034 5.982 0.000
L1.Steiermark 0.022662 0.060732 0.373 0.709
L1.Tirol 0.109139 0.049029 2.226 0.026
L1.Vorarlberg -0.081012 0.043266 -1.872 0.061
L1.Wien 0.032746 0.082604 0.396 0.692
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.013164 0.172353 0.076 0.939
L1.Burgenland -0.048268 0.096357 -0.501 0.616
L1.Kärnten 0.035399 0.049729 0.712 0.477
L1.Niederösterreich -0.207742 0.200146 -1.038 0.299
L1.Oberösterreich 0.458272 0.201790 2.271 0.023
L1.Salzburg 0.313194 0.103760 3.018 0.003
L1.Steiermark 0.107868 0.133979 0.805 0.421
L1.Tirol 0.315864 0.108160 2.920 0.003
L1.Vorarlberg 0.011008 0.095447 0.115 0.908
L1.Wien 0.011175 0.182229 0.061 0.951
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.216939 0.039835 5.446 0.000
L1.Burgenland 0.092368 0.022271 4.148 0.000
L1.Kärnten -0.005255 0.011494 -0.457 0.648
L1.Niederösterreich 0.226441 0.046259 4.895 0.000
L1.Oberösterreich 0.166086 0.046639 3.561 0.000
L1.Salzburg 0.036893 0.023982 1.538 0.124
L1.Steiermark 0.029974 0.030966 0.968 0.333
L1.Tirol 0.077986 0.024999 3.120 0.002
L1.Vorarlberg 0.055788 0.022060 2.529 0.011
L1.Wien 0.103772 0.042118 2.464 0.014
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.155116 0.039344 3.943 0.000
L1.Burgenland 0.043334 0.021996 1.970 0.049
L1.Kärnten -0.013120 0.011352 -1.156 0.248
L1.Niederösterreich 0.156855 0.045688 3.433 0.001
L1.Oberösterreich 0.342517 0.046063 7.436 0.000
L1.Salzburg 0.098759 0.023686 4.170 0.000
L1.Steiermark 0.112883 0.030584 3.691 0.000
L1.Tirol 0.089454 0.024690 3.623 0.000
L1.Vorarlberg 0.054564 0.021788 2.504 0.012
L1.Wien -0.041344 0.041598 -0.994 0.320
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.150494 0.074548 2.019 0.044
L1.Burgenland -0.033045 0.041678 -0.793 0.428
L1.Kärnten -0.036740 0.021509 -1.708 0.088
L1.Niederösterreich 0.129844 0.086570 1.500 0.134
L1.Oberösterreich 0.177323 0.087281 2.032 0.042
L1.Salzburg 0.255806 0.044880 5.700 0.000
L1.Steiermark 0.082260 0.057950 1.419 0.156
L1.Tirol 0.134406 0.046783 2.873 0.004
L1.Vorarlberg 0.104582 0.041284 2.533 0.011
L1.Wien 0.037718 0.078820 0.479 0.632
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.075192 0.058952 1.275 0.202
L1.Burgenland 0.016255 0.032958 0.493 0.622
L1.Kärnten 0.050906 0.017010 2.993 0.003
L1.Niederösterreich 0.182505 0.068459 2.666 0.008
L1.Oberösterreich 0.335193 0.069021 4.856 0.000
L1.Salzburg 0.049728 0.035491 1.401 0.161
L1.Steiermark -0.003266 0.045827 -0.071 0.943
L1.Tirol 0.125992 0.036996 3.406 0.001
L1.Vorarlberg 0.059796 0.032647 1.832 0.067
L1.Wien 0.108930 0.062331 1.748 0.081
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.173948 0.071376 2.437 0.015
L1.Burgenland 0.010170 0.039904 0.255 0.799
L1.Kärnten -0.061062 0.020594 -2.965 0.003
L1.Niederösterreich -0.111343 0.082886 -1.343 0.179
L1.Oberösterreich 0.231357 0.083567 2.769 0.006
L1.Salzburg 0.039338 0.042970 0.915 0.360
L1.Steiermark 0.262550 0.055484 4.732 0.000
L1.Tirol 0.489517 0.044792 10.929 0.000
L1.Vorarlberg 0.070016 0.039527 1.771 0.077
L1.Wien -0.101492 0.075466 -1.345 0.179
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.140266 0.079103 1.773 0.076
L1.Burgenland -0.011135 0.044224 -0.252 0.801
L1.Kärnten 0.062952 0.022824 2.758 0.006
L1.Niederösterreich 0.174322 0.091859 1.898 0.058
L1.Oberösterreich -0.082255 0.092614 -0.888 0.374
L1.Salzburg 0.222554 0.047622 4.673 0.000
L1.Steiermark 0.139470 0.061491 2.268 0.023
L1.Tirol 0.054375 0.049641 1.095 0.273
L1.Vorarlberg 0.140626 0.043807 3.210 0.001
L1.Wien 0.162295 0.083636 1.940 0.052
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459408 0.043959 10.451 0.000
L1.Burgenland 0.000528 0.024576 0.021 0.983
L1.Kärnten -0.014399 0.012684 -1.135 0.256
L1.Niederösterreich 0.181420 0.051048 3.554 0.000
L1.Oberösterreich 0.255575 0.051467 4.966 0.000
L1.Salzburg 0.019388 0.026465 0.733 0.464
L1.Steiermark -0.008626 0.034172 -0.252 0.801
L1.Tirol 0.074594 0.027587 2.704 0.007
L1.Vorarlberg 0.056834 0.024344 2.335 0.020
L1.Wien -0.023215 0.046478 -0.499 0.617
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.029858 0.092687 0.155066 0.142250 0.070310 0.081466 0.014681 0.208682
Kärnten 0.029858 1.000000 -0.030835 0.135291 0.051631 0.076199 0.454740 -0.078103 0.101576
Niederösterreich 0.092687 -0.030835 1.000000 0.292077 0.104457 0.257316 0.049606 0.149044 0.254303
Oberösterreich 0.155066 0.135291 0.292077 1.000000 0.198514 0.288880 0.155400 0.133693 0.198178
Salzburg 0.142250 0.051631 0.104457 0.198514 1.000000 0.122444 0.059638 0.112097 0.071406
Steiermark 0.070310 0.076199 0.257316 0.288880 0.122444 1.000000 0.132624 0.091854 0.012527
Tirol 0.081466 0.454740 0.049606 0.155400 0.059638 0.132624 1.000000 0.063551 0.126062
Vorarlberg 0.014681 -0.078103 0.149044 0.133693 0.112097 0.091854 0.063551 1.000000 -0.004335
Wien 0.208682 0.101576 0.254303 0.198178 0.071406 0.012527 0.126062 -0.004335 1.000000